def __init__(self, filepath: str, src, dst=None, **kwargs) -> None:
if not dst:
dst = src + '_fasttext'
self.filepath = filepath
flash(f'Loading fasttext model {filepath} [blink][yellow]...[/yellow][/blink]')
filepath = get_resource(filepath)
with stdout_redirected(to=os.devnull, stdout=sys.stderr):
self._model = fasttext.load_model(filepath)
flash('')
output_dim = self._model['king'].size
super().__init__(output_dim, src, dst)
def smatch_eval(pred, gold, use_fast=False) -> Union[SmatchScores, F1_]:
script = get_resource(_FAST_SMATCH_SCRIPT if use_fast else _SMATCH_SCRIPT)
home = os.path.dirname(script)
pred = os.path.realpath(pred)
gold = os.path.realpath(gold)
with pushd(home):
flash('Running evaluation script [blink][yellow]...[/yellow][/blink]')
cmd = f'bash {script} {pred} {gold}'
text = run_cmd(cmd)
flash('')
return format_fast_scores(text) if use_fast else format_official_scores(
text)
def load_word2vec(path, delimiter=' ', cache=True) -> Tuple[Dict[str, np.ndarray], int]:
realpath = get_resource(path)
binpath = replace_ext(realpath, '.pkl')
if cache:
try:
flash('Loading word2vec from cache [blink][yellow]...[/yellow][/blink]')
word2vec, dim = load_pickle(binpath)
flash('')
return word2vec, dim
except IOError:
pass
dim = None
word2vec = dict()
f = TimingFileIterator(realpath)
for idx, line in enumerate(f):
f.log('Loading word2vec from text file [blink][yellow]...[/yellow][/blink]')
line = line.rstrip().split(delimiter)
if len(line) > 2:
if dim is None:
dim = len(line)
else:
if len(line) != dim:
logger.warning('{}#{} length mismatches with {}'.format(path, idx + 1, dim))
continue
word, vec = line[0], line[1:]
word2vec[word] = np.array(vec, dtype=np.float32)
dim -= 1
if cache:
flash('Caching word2vec [blink][yellow]...[/yellow][/blink]')
save_pickle((word2vec, dim), binpath)
flash('')
return word2vec, dim
def load_word2vec_as_vocab_tensor(
path,
delimiter=' ',
cache=True) -> Tuple[Dict[str, int], torch.Tensor]:
realpath = get_resource(path)
vocab_path = replace_ext(realpath, '.vocab')
matrix_path = replace_ext(realpath, '.pt')
if cache:
try:
flash(
'Loading vocab and matrix from cache [blink][yellow]...[/yellow][/blink]'
)
vocab = load_pickle(vocab_path)
matrix = torch.load(matrix_path, map_location='cpu')
flash('')
return vocab, matrix
except IOError:
pass
word2vec, dim = load_word2vec(path, delimiter, cache)
vocab = dict((k, i) for i, k in enumerate(word2vec.keys()))
matrix = torch.Tensor(list(word2vec.values()))
if cache:
flash('Caching vocab and matrix [blink][yellow]...[/yellow][/blink]')
save_pickle(vocab, vocab_path)
torch.save(matrix, matrix_path)
flash('')
return vocab, matrix
def load(self, save_dir: str, devices=None, **kwargs):
save_dir = get_resource(save_dir)
# flash('Loading config and vocabs [blink][yellow]...[/yellow][/blink]')
if devices is None and self.model:
devices = self.devices
self.load_config(save_dir, **kwargs)
self.load_vocabs(save_dir)
flash('Building model [blink][yellow]...[/yellow][/blink]')
self.model = self.build_model(**merge_dict(self.config,
training=False,
**kwargs,
overwrite=True,
inplace=True))
flash('')
self.load_weights(save_dir, **kwargs)
self.to(devices)
self.model.eval()
def to(self,
devices=Union[int, float, List[int],
Dict[str, Union[int, torch.device]]],
logger: logging.Logger = None):
if devices == -1 or devices == [-1]:
devices = []
elif isinstance(devices, (int, float)) or devices is None:
devices = cuda_devices(devices)
if devices:
if logger:
logger.info(
f'Using GPUs: [on_blue][cyan][bold]{devices}[/bold][/cyan][/on_blue]'
)
if isinstance(devices, list):
flash(
f'Moving model to GPUs {devices} [blink][yellow]...[/yellow][/blink]'
)
self.model = self.model.to(devices[0])
if len(devices) > 1 and not isdebugging() and not isinstance(
self.model, nn.DataParallel):
self.model = self.parallelize(devices)
elif isinstance(devices, dict):
for name, module in self.model.named_modules():
for regex, device in devices.items():
try:
on_device: torch.device = next(
module.parameters()).device
except StopIteration:
continue
if on_device == device:
continue
if isinstance(device, int):
if on_device.index == device:
continue
if re.match(regex, name):
if not name:
name = '*'
flash(
f'Moving module [yellow]{name}[/yellow] to [on_yellow][magenta][bold]{device}'
f'[/bold][/magenta][/on_yellow]: [red]{regex}[/red]\n'
)
module.to(device)
else:
raise ValueError(f'Unrecognized devices {devices}')
flash('')
else:
if logger:
logger.info('Using CPU')
def make_gold_conll(ontonotes_path, language):
ontonotes_path = os.path.abspath(get_resource(ontonotes_path))
to_conll = get_resource(
'https://gist.githubusercontent.com/hankcs/46b9137016c769e4b6137104daf43a92/raw/66369de6c24b5ec47696ae307591f0d72c6f3f02/ontonotes_to_conll.sh'
)
to_conll = os.path.abspath(to_conll)
# shutil.rmtree(os.path.join(ontonotes_path, 'conll-2012'), ignore_errors=True)
with pushd(ontonotes_path):
try:
flash(
f'Converting [blue]{language}[/blue] to CoNLL format, '
f'this might take half an hour [blink][yellow]...[/yellow][/blink]'
)
run_cmd(f'bash {to_conll} {ontonotes_path} {language}')
flash('')
except RuntimeError as e:
flash(
f'[red]Failed[/red] to convert {language} of {ontonotes_path} to CoNLL. See exceptions for detail'
)
raise e
def fit(self,
trn_data,
dev_data,
save_dir,
batch_size,
epochs,
devices=None,
logger=None,
seed=None,
finetune=False,
eval_trn=True,
_device_placeholder=False,
**kwargs):
# Common initialization steps
config = self._capture_config(locals())
if not logger:
logger = self.build_logger('train', save_dir)
if not seed:
self.config.seed = 233 if isdebugging() else int(time.time())
set_seed(self.config.seed)
logger.info(self._savable_config.to_json(sort=True))
if isinstance(devices, list) or devices is None or isinstance(
devices, float):
flash('[yellow]Querying CUDA devices [blink]...[/blink][/yellow]')
devices = -1 if isdebugging() else cuda_devices(devices)
flash('')
# flash(f'Available GPUs: {devices}')
if isinstance(devices, list):
first_device = (devices[0] if devices else -1)
elif isinstance(devices, dict):
first_device = next(iter(devices.values()))
elif isinstance(devices, int):
first_device = devices
else:
first_device = -1
if _device_placeholder and first_device >= 0:
_dummy_placeholder = self._create_dummy_placeholder_on(
first_device)
if finetune:
if isinstance(finetune, str):
self.load(finetune, devices=devices)
else:
self.load(save_dir, devices=devices)
logger.info(
f'Finetune model loaded with {sum(p.numel() for p in self.model.parameters() if p.requires_grad)}'
f'/{sum(p.numel() for p in self.model.parameters())} trainable/total parameters.'
)
self.on_config_ready(**self.config)
trn = self.build_dataloader(**merge_dict(config,
data=trn_data,
batch_size=batch_size,
shuffle=True,
training=True,
device=first_device,
logger=logger,
vocabs=self.vocabs,
overwrite=True))
dev = self.build_dataloader(
**merge_dict(config,
data=dev_data,
batch_size=batch_size,
shuffle=False,
training=None,
device=first_device,
logger=logger,
vocabs=self.vocabs,
overwrite=True)) if dev_data else None
if not finetune:
flash('[yellow]Building model [blink]...[/blink][/yellow]')
self.model = self.build_model(**merge_dict(config, training=True))
flash('')
logger.info(
f'Model built with {sum(p.numel() for p in self.model.parameters() if p.requires_grad)}'
f'/{sum(p.numel() for p in self.model.parameters())} trainable/total parameters.'
)
assert self.model, 'build_model is not properly implemented.'
_description = repr(self.model)
if len(_description.split('\n')) < 10:
logger.info(_description)
self.save_config(save_dir)
self.save_vocabs(save_dir)
self.to(devices, logger)
if _device_placeholder and first_device >= 0:
del _dummy_placeholder
criterion = self.build_criterion(**merge_dict(config, trn=trn))
optimizer = self.build_optimizer(
**merge_dict(config, trn=trn, criterion=criterion))
metric = self.build_metric(**self.config)
if hasattr(trn.dataset, '__len__') and dev and hasattr(
dev.dataset, '__len__'):
logger.info(
f'{len(trn.dataset)}/{len(dev.dataset)} samples in trn/dev set.'
)
trn_size = len(trn) // self.config.get('gradient_accumulation', 1)
ratio_width = len(f'{trn_size}/{trn_size}')
else:
ratio_width = None
return self.execute_training_loop(**merge_dict(config,
trn=trn,
dev=dev,
epochs=epochs,
criterion=criterion,
optimizer=optimizer,
metric=metric,
logger=logger,
save_dir=save_dir,
devices=devices,
ratio_width=ratio_width,
trn_data=trn_data,
dev_data=dev_data,
eval_trn=eval_trn,
overwrite=True))